When performing 2D detection in camera images, efficient and high-performing results have been achieved using dense convolutional approaches [1]}, [2]}.
Although the sensor's range data is similar in many ways to a camera image, the desired output is an oriented bounding box in 3D space. Therefore, 3D object detectors must transform the sensor data in the range view (RV) into detections in the top-down view also known as the bird's eye view (BEV). Most existing work starts by projecting the range measurements into 3D points and then discretizing the 3D space into voxels in order to operate directly in the output space [3]}, [4]}, [5]}. These approaches have shown impressive results but require operating on sparse input data which can be computationally expensive. There has also been work operating on the dense RV representation directly [6]}, [7]}, but these methods have not matched the performance of the BEV approaches. Additionally, there has been work combining both RV and BEV representations [6]}.
